Verification system for simulating sunlight irradiation experiment on vehicle

Abstract

The invention discloses a verification system for simulating a sunlight irradiation experiment on a vehicle. The verification system comprises a full-size simulation vehicle model, a sunlight irradiation simulation device, a light sensor system, a communication module, an intelligent control module and a cloud database. The outer surface of the full-size simulation vehicle model is coated with a spectrum selective coating, vehicle window glass is electrochromic glass, the light sensor system comprises an in-vehicle light sensor, an out-vehicle light sensor, a dynamic range micro camera array and an eyeball tracking module, and the in-vehicle light sensor and the out-vehicle light sensor are arranged at specific positions respectively. And the communication module adopts a dual-redundancy wireless transmission architecture. A data analysis sub-module of the intelligent control module is integrated with an illumination feature extraction model based on a convolutional neural network, a glare analysis sub-module comprises a pupil dynamic simulation model and the like, and a light control sub-module adopts a PWM dimming technology. The cloud database constructs a three-dimensional spatio-temporal data model. According to the invention, multi-dimensional data acquisition is realized, data comprehensiveness is ensured, an illumination environment is accurately simulated, and simulation authenticity is improved.

Description

Technical Field

[0001] The present invention relates to the application field of illumination simulation testing, and in particular to a verification system for simulating sunlight irradiation experiments on vehicles. Background Art

[0002] In automotive R&D and testing, simulated sunlight exposure experiments are crucial for evaluating key performance indicators such as vehicle optical performance, interior material durability, and driver visual comfort. These experiments aim to create a controlled lighting environment and analyze the vehicle's response to varying lighting conditions, providing data support for optimizing vehicle design and improving driving safety and comfort. However, traditional simulated sunlight exposure testing systems have certain technical limitations.

[0003] Common lighting experiment systems can only achieve single-dimensional lighting data collection. For example, they rely on only a small number of light sensors to monitor the light intensity inside or outside the vehicle. This makes it difficult to fully capture the details of light reflection and refraction on the vehicle surface and the glare effect on human visual perception. This leads to incomplete data collection coverage and the inability to form a full-chain association between the environment, vehicle, and human perception. At the same time, traditional simulation devices often only focus on adjusting light intensity and lack the simultaneous simulation of related environmental factors such as temperature. This causes differences between the experimental environment and the actual sunlight exposure scene, affecting the authenticity of the data. In addition, in the data processing and control links, the analysis accuracy of the existing system is insufficient. It is impossible to accurately adjust the interior lighting system according to the real-time collected light data, making it difficult to accurately evaluate the vehicle status under complex lighting conditions. This restricts the guiding value of the experimental results for actual vehicle design and cannot meet the working requirements of lighting simulation test applications. Therefore, a verification system for simulating sunlight exposure experiments on vehicles is proposed. Summary of the Invention

[0004] The present invention provides the following technical solution: a verification system for simulating sunlight exposure experiments on a vehicle, comprising: A full-scale simulation vehicle model, a sunlight exposure simulation device, a light sensor system, a communication module, an intelligent control module, and a cloud database. The full-scale simulation vehicle model is used to simulate a real vehicle for sunlight exposure experiments. A sunlight simulation device is used to simulate sunlight shining on the simulated vehicle model to provide a controllable lighting environment. The light sensor system includes an in-vehicle light sensor, an out-vehicle light sensor, a dynamic range micro-camera array, and an eye tracking module. The in-vehicle light sensor and the out-vehicle light sensor are both wirelessly connected to the communication module. a communication module for transmitting light data information collected by the light sensor system in real time. The communication module is connected to the light sensor system. The intelligent control module includes a data analysis submodule, a glare analysis submodule, and a light control submodule. The data analysis submodule is used to receive and analyze the light data information transmitted by the communication module and send instructions to the light control submodule based on the analysis results. The light control module is used to adjust the brightness of the interior lights in the full-scale simulated vehicle model. A cloud database is used to store data information processed by the intelligent control system to form a big data model. The cloud database is connected to the intelligent control system. An infrared heating array and a thermal radiation controller are additionally provided inside the sunlight simulation device. The thermal radiation controller is electrically connected to the intelligent control module. The thermal radiation controller is used to synchronously adjust the infrared heating power according to the current simulated light intensity.

[0005] Preferably, the outer surface of the full-size simulated car model is coated with a spectrally selective coating, and the window glass of the simulated car model is made of electrochromic glass. The transmittance of the electrochromic glass is dynamically adjusted by the intelligent control module according to data fed back by the light sensor system.

[0006] Preferably, the sunlight simulation device is internally provided with 3-6 groups of independently controllable light source arrays, and the light source arrays are composed of xenon lamps and LED light sources. The LED light sources are internally integrated with white light chips with adjustable color temperature and monochromatic light auxiliary chips, and the infrared heating array adopts carbon fiber heating tubes with honeycomb distribution.

[0007] Preferably, a spectral filter is arranged in front of the light source array of the sunlight simulation device, and the spectral filter is composed of a liquid crystal tunable filter. The thermal radiation controller is integrated with a fuzzy PID control algorithm, and the fuzzy PID control algorithm is used to automatically optimize the infrared heating spatial distribution according to the thermal image of the surface of the simulated vehicle model.

[0008] Preferably, the interior light sensors in the light sensor system are arranged at the driver's eye height area, the center console operation interface and the back of the rearview mirror, and the exterior light sensors are distributed at the front windshield, side windows and sunroof.

[0009] Preferably, the communication module adopts a dual-redundant wireless transmission architecture, the main link of the dual-redundant wireless transmission architecture is the 5G NR communication protocol, the backup link of the dual-redundant wireless transmission architecture adopts Sub-GHz frequency band private network communication, and the communication module has a built-in adaptive modulation and coding unit.

[0010] Preferably, the data analysis submodule of the intelligent control module is internally integrated with a light feature extraction model based on a convolutional neural network, and the light control submodule adopts PWM dimming technology.

[0011] Preferably, the glare analysis submodule of the intelligent control module includes a pupil dynamic simulation model, a reflected light path tracing engine and a risk level assessment unit. The pupil dynamic simulation model is based on the gaze point data collected by the eye tracking module and calculates the pupil diameter change in real time in combination with the light intensity. The reflected light path tracing engine adopts a Monte Carlo ray tracing algorithm to simulate the reflection path of sunlight at the interface of the car window and the dashboard.

[0012] Preferably, a three-dimensional spatiotemporal data model is constructed inside the cloud database, and the storage structure of the cloud database includes the spatiotemporal distribution field of light intensity, thermal radiation power spectrum and glare event timestamp.

[0013] Preferably, the cloud database includes a data hierarchical storage architecture, an incremental update index system and a cross-domain correlation analysis engine. The data hierarchical storage architecture adopts a hot and cold data separation design, and the incremental update index system adopts an LSM tree structure.

[0014] In summary, compared with the prior art, the present invention provides a verification system for simulating sunlight exposure experiments on vehicles, which has the following beneficial effects: 1. The present invention achieves multi-dimensional data collection of the lighting environment through the collaborative work of in-vehicle light sensors, exterior light sensors, a dynamic range micro-camera array, and an eye-tracking module. Specifically, the in-vehicle light sensors and exterior light sensors can synchronously monitor light intensity and spectral distribution parameters, while the dynamic range camera array captures the reflection and refraction details of light on the surface of the simulated vehicle model. The eye-tracking module simulates the glare perception path from the driver's perspective, forming a data closed loop covering the entire chain of environmental, vehicle, and human perception, thereby obtaining richer visual-level lighting data. The eye-tracking module can also track the human eye's response to light during the simulation process, supplementing the light data from the perspective of human-computer interaction. The multi-dimensional sensors and modules work together to ensure the comprehensive collection of light data information, covering the lighting conditions of the vehicle at all directions and levels under sunlight, providing a comprehensive data foundation for the experiment. 2. The present invention provides a controllable lighting environment through a sunlight simulation device, which can accurately simulate sunlight conditions at different times and locations. The infrared heating array and thermal radiation controller added to the device can synchronously adjust the infrared heating power according to the current simulated light intensity, making the simulated lighting environment closer to a real sunlight scene not only in terms of light intensity but also in terms of related environmental factors such as temperature, thereby improving the authenticity of the simulated environment. At the same time, the data analysis submodule in the intelligent control module accurately analyzes the real-time transmitted light data information. Combined with the professional analysis of glare conditions by the glare analysis submodule, it can accurately determine the lighting status of the vehicle under sunlight. Based on the analysis results, precise instructions are sent to the lighting control submodule to achieve precise adjustment of the interior light brightness in the full-scale simulation vehicle model, ensuring the accuracy of various control operations during the experiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1 The present invention provides a technical solution, a verification system for simulating sunlight exposure experiments on a vehicle, comprising: A full-scale simulated vehicle model, a sunlight exposure simulation device, a light sensor system, a communication module, an intelligent control module, and a cloud database are provided. The full-scale simulated vehicle model is used to simulate a real vehicle in a sunlight exposure experiment. The outer surface of the full-scale simulated vehicle model is coated with a spectrally selective coating. The window glass of the simulated vehicle model is made of electrochromic glass. The light transmittance of the electrochromic glass is dynamically adjusted in real time by the intelligent control module based on data feedback from the light sensor system. The specific implementation of the above method is as follows: Multi-source data collection: Light sensor arrays deployed inside and outside the vehicle continuously capture lighting data. External sensors monitor ambient light intensity, spectral composition, and incident angle. Internal sensors simultaneously record the actual luminous flux in each area of ​​the cockpit. A dynamic range camera array captures diffuse and specular reflection details from the window glass surface at a high frame rate. An eye tracking module collects the tester's gaze coordinates and pupil dynamic parameters in real time, and combines this with head posture data to construct a three-dimensional gaze trajectory, providing perceptual data at the human-computer interaction level for glare analysis. Multimodal data fusion transmission: All sensor nodes send data in parallel through dual redundant wireless transmission channels. The primary link uses the 5G NR protocol to ensure low-latency transmission, while the backup link uses a sub-GHz private network to ensure data integrity in harsh electromagnetic environments. The adaptive modulation and coding unit dynamically switches the modulation mode based on channel quality, ensuring real-time data while optimizing transmission power consumption. During the intelligent analysis and processing phase, the convolutional neural network in the data analysis submodule extracts multi-dimensional features from the raw light signal, outputting a light intensity distribution map and spectral energy density curve. The glare analysis submodule simultaneously activates dual analysis engines: a pupil dynamic simulation model combines gaze point data with real-time light intensity to calculate the equivalent glare index within the current field of view; and a reflected light path tracing engine employs a Monte Carlo algorithm to simulate the propagation paths of millions of light rays across glass and interior surfaces, accurately locating potential glare sources. During the transmittance decision execution phase, the intelligent control module integrates optical analysis and thermal radiation data to generate a three-dimensional spatial transmittance adjustment instruction set. This instruction set includes independent control parameters for each window zone, implementing differentiated adjustment strategies for the driver's primary field of view, side field of view, and rearview mirror reflection area. The lighting control submodule uses PWM dimming technology to apply precise electrical signals to the electrochromic glass, driving the redox reaction of the electrochromic material in the glass, achieving continuous and gradual transmittance adjustment within millisecond delays. Closed-loop verification and optimization phase: The adjusted lighting environment parameters are collected again by the light sensor system, forming a closed-loop control loop of "adjustment-feedback-readjustment". The thermal radiation controller synchronously adjusts the output power of the infrared heating array according to changes in light intensity. The fuzzy PID algorithm is used to optimize the thermal field distribution inside the vehicle to ensure the physical consistency of the light-thermal coupling environment and the real scene. All process data is compressed and encrypted before being uploaded to the cloud database. The spatiotemporal data model continuously accumulates typical operating condition data sets, providing a foundation for deep learning training of the subsequent adjustment algorithm. A sunlight irradiation simulation device is used to simulate sunlight irradiating a simulated vehicle model and provide a controllable lighting environment. The sunlight irradiation simulation device is internally provided with 3-6 independently controllable light source arrays, each composed of a xenon lamp and an LED light source. The LED light source is internally integrated with a white light chip with adjustable color temperature and a monochromatic light auxiliary chip. The infrared heating array uses carbon fiber heating tubes distributed in a honeycomb pattern. A spectral filter is configured in front of the light source array of the sunlight irradiation simulation device. The spectral filter is composed of a liquid crystal tunable filter. The thermal radiation controller is integrated with a fuzzy PID control algorithm. The fuzzy PID control algorithm is used to automatically optimize the infrared heating spatial distribution based on the thermal image of the surface of the simulated vehicle model. The specific implementation process of the above method is as follows; Initial spectrum construction stage: The xenon lamp in the light source array works in conjunction with the multi-color temperature LED. The xenon lamp provides a continuous spectrum base. The LED white light chip achieves color temperature coverage of 2000K-6500K through current regulation. The monochromatic light auxiliary chip enhances the output of specific bands. The liquid crystal tunable filter array is initialized to the full-pass state, allowing the composite light source to pass completely. At this time, the spectral distribution is aligned with the standard solar spectrum curve. Real-time spectral acquisition and analysis: The spectral analysis unit in the external light sensor array continuously collects simulated ambient light and decomposes it into energy proportion data for ultraviolet, visible light, and infrared bands. The dynamic range camera array captures the reflected light from the vehicle surface and uses multi-exposure fusion technology to obtain high dynamic range spectral images, identifying abnormal spectral peaks or missing bands. The spectral analysis engine of the intelligent control module compares the measured spectrum with the preset target spectrum and calculates the energy deviation value of each band. The liquid crystal tunable filter drive circuit receives the correction command and controls the orientation of the liquid crystal molecules through the electric field. The light transmission band range of each filter unit is adjusted within a millisecond delay, attenuating the bands that exceed the standard and compensating for the missing bands. Thermal radiation data acquisition: The infrared thermal imager scans the surface of the simulated vehicle model at a frame rate of 60 FPS, generating a 256-level grayscale thermal distribution map. Key heat-absorbing areas, such as the roof, front windshield, and metal trim, are monitored. The thermal image data undergoes spatial alignment and pixel-level mapping with the vehicle's three-dimensional model to establish a temperature-position coordinate correlation matrix. Fuzzy control decision-making stage: The thermal radiation controller reads the current thermal image and extracts the temperature deviation, deviation change rate, and spatial distribution gradient as fuzzy input variables. The fuzzy processing unit converts the precise numerical value into language variables such as "low temperature, medium temperature, and high temperature". It then performs reasoning based on a preset library of 49 control rules. The defuzzification module uses the center of gravity method to convert the fuzzy output into a specific power adjustment value, generating a control instruction set for 128 independent heating units. Dynamic heating power allocation: The honeycomb carbon fiber heating tube array receives zone control commands and achieves 0-100% stepless power adjustment through pulse width modulation technology, focusing on enhancing heating intensity on the sun-facing side of the vehicle body. The intelligent control module continuously compares the thermal image with the target temperature field, making fine adjustments to the control amount every 200ms to ensure that the surface temperature gradient of the vehicle body is consistent with the heat conduction characteristics under natural light conditions. During the photothermal synergy verification phase, the light sensor system and the thermal imager are cross-validated. When an abnormal photothermal coupling effect is detected in a specific area, a joint optimization program is triggered. The fuzzy PID controller dynamically adjusts the weights of the proportional, integral, and differential parameters, prioritizing temperature field stability in scenarios with sudden changes in illumination. Under steady-state conditions, the spectral matching accuracy is enhanced, forming a closed loop for collaborative control of the photothermal and thermal parameters. The light sensor system includes an interior light sensor, an exterior light sensor, a dynamic range micro-camera array, and an eye tracking module. Both the interior and exterior light sensors are wirelessly connected to the communication module. The interior light sensors are located at the driver's eye level, on the center console, and behind the rearview mirror. The exterior light sensors are located on the front windshield, side windows, and sunroof. The communication module is used to transmit the light data information collected by the light sensor system in real time. The communication module is connected to the light sensor system and adopts a dual-redundant wireless transmission architecture. The primary link of the dual-redundant wireless transmission architecture uses the 5G NR communication protocol, and the backup link of the dual-redundant wireless transmission architecture uses a sub-GHz frequency band private network communication. The communication module has a built-in adaptive modulation and coding unit. The specific implementation process of the above function is as follows; Data acquisition and preprocessing: Each node in the light sensor system synchronously collects light intensity, spectral distribution, and glare trajectory data. The dynamic range camera array generates a JPEG XR format image stream with a high compression ratio. The edge computing unit built into the sensor node performs spatiotemporal alignment on the raw data, encapsulating the multi-source heterogeneous data into a standard UDP data packet, adding a timestamp and check digit to form a complete data frame. During the primary link priority transmission phase, data packets are first injected into the 5G NR communication link. Carrier aggregation technology is used to simultaneously utilize the 3.5 GHz and 28 GHz millimeter wave frequency bands to achieve Gbps-level transmission rates. The adaptive modulation and coding unit monitors channel quality indicators in real time, automatically switching to 256QAM high-order modulation in strong signal environments and downgrading to QPSK modulation when multipath interference is detected to ensure transmission stability. During the link health monitoring phase, the communication module's built-in link monitoring agent continuously collects three key indicators: the signal-to-noise ratio, bit error rate, and number of retransmissions of the primary link. If it detects five consecutive data packet transmission delays exceeding 10ms, or if the bit error rate exceeds the 0.1% threshold, it automatically triggers a link switching decision process. Backup link activation phase: The system seamlessly switches to the sub-GHz private network link, which operates in the 470MHz frequency band and uses GFSK modulation to achieve penetrating transmission. The private network communication protocol uses the time division multiple access (TDMA) mechanism to allocate independent time slots to each sensor node, avoiding co-frequency interference problems in industrial environments. Dual-link collaborative working mode: In complex electromagnetic environments, the system activates dual-link concurrent transmission mode. Important control instructions are issued via the 5G link, and large-capacity sensor data is transmitted via the sub-GHz link. The receiving end uses a data fusion engine to align the timing and compare the content of redundant data packets arriving from the dual links, automatically removing duplicate frames and repairing transmission errors. Adaptive coding optimization: The adaptive modulation and coding unit dynamically adjusts the forward error correction (FEC) coding strength based on real-time channel assessment results. It uses low-redundancy LDPC coding when channel quality is good, and automatically switches to high-redundancy Turbo coding when sudden interference is detected. The coding strategy adjustment cycle is less than 50ms, ensuring optimal transmission efficiency in dynamic scenarios such as fast-moving vehicles or equipment obstruction. Transmission quality closed-loop control: The receiving end regularly feeds back acknowledgment characters (ACKs) and channel quality reports, forming a closed-loop control system of "send, monitor, and adjust." When congestion occurs on both links simultaneously, the system initiates a QoS-based traffic scheduling mechanism to prioritize transmission bandwidth for critical service flows such as glare analysis data. The intelligent control module includes a data analysis submodule, a glare analysis submodule, and a lighting control submodule. The data analysis submodule is used to receive and analyze the light data information transmitted by the communication module and send instructions to the lighting control submodule based on the analysis results. The lighting control module is used to adjust the brightness of the interior lights in the full-scale simulated car model. The data analysis submodule of the intelligent control module integrates a lighting feature extraction model based on a convolutional neural network. The lighting control submodule adopts PWM dimming technology. The glare analysis submodule of the intelligent control module includes a pupil dynamic simulation model, a reflected light path tracking engine, and a risk level assessment unit. The pupil dynamic simulation model is based on the gaze point data collected by the eye tracking module and calculates the pupil diameter change in real time based on the light intensity. The reflected light path tracking engine uses the Monte Carlo ray tracing algorithm to simulate the reflection path of sunlight on the car window and dashboard interface. In the verification system of the above-mentioned simulated sunlight exposure experiment, the operation of the intelligent control module covers a complete closed loop from data collection, feature extraction, glare analysis, instruction generation to terminal adjustment. Its detailed implementation process is as follows: Real-time aggregation and preprocessing of multi-source data: The light sensor system, serving as the front-end for data collection, first acquires multi-dimensional information through a distributed sensor network. Interior light sensors are precisely positioned at the driver's eye level, on the center console, and behind the rearview mirror. These locations directly correlate with the driver's visual experience and the lighting conditions in key operating areas. Exterior light sensors are located on the windshield, side windows, and sunroof, key interfaces between the vehicle and the external light environment. They capture essential data such as incident light intensity and spectral composition from different directions in real time. A dynamic range micro-camera array, with a high-density pixel layout, continuously captures the vehicle's surface and surroundings, recording details of light reflection and refraction on various surfaces, including the vehicle's curved surfaces, windows, and instrument panel, generating visual image data containing light and shadow distribution. The eye tracking module uses non-contact infrared imaging technology to track the driver's gaze in real time, capturing the gaze position and focus state under various lighting conditions. The analog signals collected by all sensors undergo analog-to-digital conversion and are then transmitted in real time by the communication module via a dual-redundant wireless transmission architecture. The primary link utilizes an enhanced fifth-generation mobile network communication protocol to ensure high-speed, low-latency data transmission; the backup link relies on dedicated sub-gigahertz frequency band communications to provide a stable signal path in complex electromagnetic environments. The communication module's built-in adaptive modulation and coding unit continuously monitors channel quality, dynamically adjusting the encoding method and transmission rate based on signal strength and interference, avoiding data loss or delays and ensuring that optical data reaches the intelligent control module with pristine accuracy. Deep feature analysis of the data analysis submodule: As the "brain" of intelligent control, the data analysis submodule first performs fusion pre-processing on the received multi-source data. For the numerical data of the light sensors inside and outside the vehicle, the system will perform spatiotemporal calibration, unify the acquisition time of sensors in different positions to the experimental reference time axis, and generate a three-dimensional distribution field of light intensity around the vehicle through a spatial interpolation algorithm. For the image data output by the dynamic range micro-camera array, the illumination feature extraction model based on the convolutional neural network begins to play a role: the model automatically identifies key features such as highlight areas, shadow boundaries, and reflected light spots in the image through layer-by-layer scanning of multi-layer convolution kernels, and accurately locates the reflection focus and refraction path of sunlight on the vehicle body surface; at the same time, combined with the material parameters of the spectrally selective coating and electrochromic glass, it analyzes the absorption, reflection and transmission laws of light of different wavelengths on the vehicle surface, and extracts deep feature parameters such as the spatial distribution of light intensity, spectral energy ratio, and polarization state of reflected light. These feature data refined by the neural network provide a high-precision input basis for subsequent glare analysis and lighting control; Multi-level risk assessment of the glare analysis submodule: The glare analysis submodule uses the characteristic data output by the data analysis submodule as its foundation, constructing a dual analysis system that integrates human visual physiological responses and the physical path of light propagation. First, the pupil dynamic simulation model uses gaze point data collected by the eye tracking module and real-time light intensity parameters to simulate the physiological adjustment process of the human pupil under different lighting conditions. When strong light is incident, the model calculates the contraction amplitude of the pupil diameter based on the logarithmic variation of light intensity, thereby assessing the intensity of light stimulation received by the human retina. When the line of sight shifts to reflective surfaces such as the instrument panel, the model simultaneously tracks the dynamic changes of the gaze point, analyzes the pupil's adaptation process to local areas of strong light, and quantifies the impact of visual persistence and contrast changes on the driver's visual clarity. The reflected light path tracing engine uses a Monte Carlo ray tracing algorithm to perform a full-link simulation of the reflection path of sunlight on interfaces such as window glass, instrument panel surfaces, and rearview mirrors. The engine first generates statistically significant light samples based on the light source parameters of the sunlight simulation device (such as the color temperature and light intensity distribution of the xenon lamp and LED light source). It then sequentially calculates the reflection, refraction, and absorption behavior of each ray of light at different interfaces, targeting physical parameters such as the real-time transmittance of the electrochromic glass, the surface roughness of the instrument panel, and the tilt angle of the window. Ultimately, it constructs the complete propagation path of light from the outside world to the driver's eyes. By simulating millions of light samples, the engine can accurately locate reflection points that may cause glare, such as windshield reflections that blur the instrument panel information, or side windows that reflect sunlight directly into the driver's eyes. Precise dynamic adjustment of the lighting control submodule: Acting as the execution terminal, the lighting control submodule uses pulse-width modulation dimming technology to achieve refined control of the vehicle's interior lighting based on the feature analysis results of the data analysis submodule and the risk assessment report from the glare analysis submodule. First, the system determines a baseline brightness threshold for the interior lighting based on the currently simulated lighting scenario (e.g., midday glare, dusk backlight), combined with real-time brightness data from the vehicle's light sensors. Then, for high-risk areas identified in the glare analysis report, such as the reflective areas of the instrument panel in front of the driver's line of sight, the system automatically generates a localized brightness compensation scheme. By adjusting the pulse-width duty cycle of the interior lighting in these areas, the system enhances illumination uniformity in the target area and mitigates visual interference caused by reflected light without affecting the overall lighting environment. During this adjustment process, the lighting control submodule receives dynamic commands from the data analysis submodule in real time. For example, if the sunlight simulator adjusts the illumination angle, causing shadowed areas within the vehicle to change, the system will synchronously adjust the interior lighting's intensity and distribution pattern to ensure optimal visibility of the driver's interface. At the same time, this module is also linked to the transmittance adjustment function of the electrochromic glass: when it detects that strong light from outside the vehicle causes the glass transmittance to decrease, the interior lights will automatically increase the brightness to maintain the stability of the visual environment inside the vehicle and avoid visual fatigue caused by excessive light and dark contrast. Full-process closed-loop control and data sedimentation: After completing a round of data processing and instruction execution, the intelligent control module will package and transmit data such as lighting characteristic parameters, glare assessment results, and lighting adjustment strategies to the cloud database. The cloud database uses a three-dimensional spatiotemporal data model to structure the storage of information such as the spatiotemporal distribution field of light intensity, thermal radiation power spectrum, and glare event timestamps at different experimental stages, and uses a cross-domain correlation analysis engine to explore the potential correlation between the lighting environment and the optical performance of the vehicle and the visual response of the human body. These sedimented big data models not only provide a real-time reference for the current experiment, but also continuously optimize the feature extraction capability of the convolutional neural network model, the simulation accuracy of the Monte Carlo ray tracing, and the adjustment logic of the pulse width modulation strategy through machine learning algorithms, forming a virtuous closed loop of "data acquisition-intelligent analysis-precision control-model optimization", ensuring that the entire verification system continuously improves the accuracy and reliability of the simulation experiment in the long term; The cloud database is used to store data information processed by the intelligent control system to form a big data model. The cloud database is connected to the intelligent control system. An infrared heating array and a thermal radiation controller are added to the interior of the sunlight simulation device. The thermal radiation controller is electrically connected to the intelligent control module. The thermal radiation controller is used to synchronously adjust the infrared heating power according to the current simulated light intensity. A three-dimensional spatiotemporal data model is constructed inside the cloud database. The storage structure of the cloud database includes the spatiotemporal distribution field of light intensity, the thermal radiation power spectrum and the glare event timestamp. The cloud database includes a data hierarchical storage architecture, an incremental update index system and a cross-domain correlation analysis engine. The data hierarchical storage architecture adopts a hot and cold data separation design, and the incremental update index system adopts an LSM tree structure.

[0018] This solution achieves multi-dimensional data collection of the lighting environment through the collaborative work of in-vehicle light sensors, external light sensors, a dynamic range micro-camera array, and an eye-tracking module. Specifically, the in-vehicle and external light sensors can simultaneously monitor light intensity and spectral distribution parameters, while the dynamic range camera array captures the reflection and refraction details of light on the surface of the simulated vehicle model. The eye-tracking module simulates the glare perception path from the driver's perspective, forming a data closed loop covering the entire chain of environmental, vehicle, and human perception, thereby obtaining richer visual-level lighting data. The eye-tracking module can also track the human eye's response to light during the simulation process, supplementing light data from the perspective of human-computer interaction. The multi-dimensional sensors and modules work together to ensure the comprehensive collection of light data information, covering the lighting conditions of the vehicle in all directions and at different levels under sunlight, providing a comprehensive data foundation for the experiment.

[0019] This solution provides a controllable lighting environment through a sunlight simulation device, which can accurately simulate sunlight conditions at different times and locations. The additional infrared heating array and thermal radiation controller inside it can synchronously adjust the infrared heating power according to the current simulated light intensity, making the simulated lighting environment closer to the actual sunlight scene not only in terms of light intensity but also in related environmental factors such as temperature, thereby improving the authenticity of the simulated environment. At the same time, the data analysis submodule in the intelligent control module accurately analyzes the real-time transmitted light data information. Combined with the professional analysis of glare conditions by the glare analysis submodule, it can accurately judge the lighting status of the vehicle under sunlight. Based on the analysis results, precise instructions are sent to the lighting control submodule to achieve precise adjustment of the interior light brightness in the full-scale simulation vehicle model, ensuring the accuracy of various control operations during the experiment.

[0020] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0021] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A verification system for simulating sunlight exposure to a vehicle, characterized in that: include: A full-scale simulation vehicle model, a sunlight exposure simulation device, a light sensor system, a communication module, an intelligent control module, and a cloud database. The full-scale simulation vehicle model is used to simulate a real vehicle for sunlight exposure experiments. A sunlight simulation device is used to simulate sunlight shining on the simulated vehicle model to provide a controllable lighting environment. The light sensor system includes an in-vehicle light sensor, an out-vehicle light sensor, a dynamic range micro-camera array, and an eye tracking module. The in-vehicle light sensor and the out-vehicle light sensor are both wirelessly connected to the communication module. a communication module for transmitting light data information collected by the light sensor system in real time. The communication module is connected to the light sensor system. The intelligent control module includes a data analysis submodule, a glare analysis submodule, and a light control submodule. The data analysis submodule is used to receive and analyze the light data information transmitted by the communication module and send instructions to the light control submodule based on the analysis results. The light control module is used to adjust the brightness of the interior lights in the full-scale simulated vehicle model. A cloud database is used to store data information processed by the intelligent control system to form a big data model. The cloud database is connected to the intelligent control system. An infrared heating array and a thermal radiation controller are additionally provided inside the sunlight simulation device. The thermal radiation controller is electrically connected to the intelligent control module. The thermal radiation controller is used to synchronously adjust the infrared heating power according to the current simulated light intensity.

2. The verification system for simulating sunlight exposure to a vehicle according to claim 1, characterized in that: The outer surface of the full-size simulated car model is coated with a spectrally selective coating, and the window glass of the simulated car model is made of electrochromic glass. The light transmittance of the electrochromic glass is dynamically adjusted by the intelligent control module according to data fed back by the light sensor system.

3. The verification system for simulating sunlight exposure to a vehicle according to claim 1, characterized in that: The sunlight simulation device is internally provided with 3-6 groups of independently controllable light source arrays, each of which is composed of a xenon lamp and an LED light source. The LED light source is internally integrated with a white light chip with adjustable color temperature and a monochromatic light auxiliary chip. The infrared heating array uses a honeycomb-shaped distributed carbon fiber heating tube.

4. The verification system for simulating sunlight exposure to a vehicle according to claim 3, characterized in that: A spectral filter is configured in front of the light source array of the sunlight simulation device, and the spectral filter is composed of a liquid crystal tunable filter. The thermal radiation controller is integrated with a fuzzy PID control algorithm, and the fuzzy PID control algorithm is used to automatically optimize the infrared heating spatial distribution according to the thermal image of the surface of the simulated vehicle model.

5. The verification system for simulating sunlight exposure to a vehicle according to claim 1, characterized in that: The interior light sensors in the light sensor system are arranged at the driver's eye height area, the center console operation interface and the back of the rearview mirror, and the exterior light sensors are distributed on the front windshield, side windows and sunroof.

6. The verification system for simulating sunlight exposure to a vehicle according to claim 1, characterized in that: The communication module adopts a dual-redundant wireless transmission architecture, the main link of the dual-redundant wireless transmission architecture is the 5G NR communication protocol, the backup link of the dual-redundant wireless transmission architecture adopts Sub-GHz frequency band private network communication, and the communication module has a built-in adaptive modulation and coding unit.

7. The verification system for simulating sunlight exposure to a vehicle according to claim 1, characterized in that: The data analysis submodule of the intelligent control module is internally integrated with a light feature extraction model based on a convolutional neural network, and the light control submodule adopts PWM dimming technology.

8. The verification system for simulating sunlight exposure to a vehicle according to claim 1, characterized in that: The glare analysis submodule of the intelligent control module includes a pupil dynamic simulation model, a reflected light path tracing engine, and a risk level assessment unit. The pupil dynamic simulation model is based on the gaze point data collected by the eye tracking module and calculates the pupil diameter change in real time in combination with the light intensity. The reflected light path tracing engine uses a Monte Carlo ray tracing algorithm to simulate the reflection path of sunlight at the interface between the car window and the dashboard.

9. The verification system for simulating sunlight exposure to a vehicle according to claim 1, characterized in that: A three-dimensional spatiotemporal data model is constructed inside the cloud database, and the storage structure of the cloud database includes the spatiotemporal distribution field of light intensity, thermal radiation power spectrum and glare event timestamp.

10. The verification system for simulating sunlight exposure to a vehicle according to claim 1, characterized in that: The cloud database includes a data hierarchical storage architecture, an incremental update index system and a cross-domain correlation analysis engine. The data hierarchical storage architecture adopts a cold and hot data separation design, and the incremental update index system adopts an LSM tree structure.

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